Operator Efficiency Improvements from Novel Human-Machine Interfaces
Mark ELTON1 and Dr. Wayne BOOK2
Georgia Institute of Technology
Woodruff School of Mechanical Engineering
771 Ferst Drive
Atlanta, Georgia
USA
Email: mark.elton@gatech.edu, wayne.book@me.gatech.edu
State of the art human-machine interfaces for multi-DOF mobile hydraulic machines are nonintuitive and require long periods of training and experience for operators to perfect control of the
machine. This paper explores using a novel human-machine interface to control a simulated 5-ton
mini-excavator. After a brief discussion of the simulation and the tests performed, it reports on
improvements in operator productivity and efficiency of the novel interface over the standard
interface. A new measure of fuel efficiency in terms of fuel per task is explained, and the test results
show that a more intuitive input device increases fuel efficiency.
Keywords: Excavator, Human-Machine Interface, Simulator, Fuel Efficiency
Introduction
Operators of mobile, multi-degree of freedom, hydraulic equipment commonly used in construction,
forestry, agriculture, and mining must have hours of training and experience to truly become expert
operators. Such multi-degree of freedom equipment has non-intuitive kinematics and dynamics.
Industry studies show that even when an operator is truly experienced, he/she continually makes
small errors because of the large cognitive demand during operation Stephenson, D. (2009). By
using a more intuitive human machine interface (HMI) that does not require the operator to do the
inverse kinematics, the number of operator errors can be reduced and operator effectiveness
increased. A more intuitive HMI allows the operator to complete the same task in less time resulting
in greater productivity and fuel efficiency. For example, coordinated control and other methods
have already been applied to multi-DOF systems, fluid-powered and otherwise, and have been
shown to increase operator control and efficiency Johnson, D. W.; Lovell, G. H.; Murray, J.J.
(1997).
In order to truly test the efficiency of HMIs, the standard and new interfaces must be tested against
one another on machines doing the same task in the same environment. Changing the controls on a
real machine is time consuming and can be expensive. In order to bypass these difficulties, a
simulator was constructed where the different HMIs could easily be switched in and out for testing
purposes. Simulation also allows the environment to be standardized for all tests. The excavator
simulator discussed in this paper is a pump controlled Bobcat mini-excavator constructed at Purdue.
Williamson, C.; Zimmerman, J.; Ivantysynova, M. (2008).
To test the operator effectiveness and fuel efficiency of the two different HMIs, 25 subjects
performed a trenching task with the simulated mini-excavator using each of the two input devices.
excavator simulator
1
2
PhD student
HUSCO Chair of Fluid Power and Motion Control
In order to standardize the testing procedure and allow for quick modifications to the machine,
environment, and control algorithms, an excavator simulator was constructed. The machine of
interest is a modified Bobcat 435 mini-excavator, a five ton machine that has been modified to be
pump-controlled rather than the standard, valve-controlled. It is one of the integrated test beds of
the Center for Compact and Efficient Fluid Power (CCEFP).
Fig. 1: The pump-controlled Bobcat 435 excavator modelled in simulation
Simulation Software
A full dynamic model of the excavator’s hydraulic and mechanical systems was constructed in
Simulink and runs in real time at 1 kHz on an xPC target. A new soil interaction model was
developed that calculated the soil-bucket interaction forces for any trajectory through the soil. A full
description of the dynamic model and soil interaction model can be found in Elton, M. (2009). The
xPC target was networked to another computer that displayed the simulated arm to the operator.
The graphics program displays a 1080x1920 image that refreshes at 60 Hz. The program also plays
continuous engine noise that varies in volume proportional to the power demand of the pumps.
Fig. 2: Screenshot from excavator simulator
Operator Workstation
A special operator workstation was constructed to give the operator a realistic feel of operating an
excavator. The operator sits in the cab of a Bobcat 435 mini-excavator and views the simulation on
a 52-inch television screen mounted onto the front windshield of the excavator. Two speakers
mounted behind the seat play the engine noise.
The original excavator was equipped with hydraulic joysticks. These were removed and electronic
joysticks (Sauer-Danfoss model JS6000) were mounted on improvised supports near the location of
the hydraulic joysticks. The electronic joysticks provided the first way to control the simulated
excavator.
Fig. 3: Phantom and right hand joystick mounted and in use in the operator workstation
A Phantom Premium 1.0 (or simply, Phantom) was the second device used to control the simulated
excavator. The Phantom model used here is a commercially available 6-DOF device with three
degrees of force feedback. The Phantom linkage and excavator linkage are similar, except where the
excavator has only one degree of freedom at its wrist (the bucket curl) the Phantom has three. Two
of these three degrees of freedom were not used to control the simulator. As the Phantom moves
about in space, commands are given to the excavator pumps so that the excavator wrist tracks the
position commanded by the position of the Phantom wrist using a PID loop. The curl of the bucket
is controlled by rotating the handle connected to the Phantom’s wrist.
The Phantom is mounted on the right side of the cab on a tray that is welded to the wall of the cab.
Although the Phantom configuration resembles an excavator, the Phantom is mounted facing
towards the operator so that it does not interfere with the operators arm. Despite the kinematic
similarities, the excavator mimics the motions of the operator’s arm and not the Phantom’s arm.
Hence, as the operator moves his hand away from himself, the excavator arm extends. If he brings
his hand in, the excavator arm comes in, if he moves his hand left, the excavator moves left, etc.
Also, as he curls his wrist (which rotates the handle) the bucket curls, and as he uncurls his wrists,
the bucket does the same.
Test Subjects and Procedure
A 25 subject test was conducted to compare the two interfaces. After filling out a brief survey to
gather demographic information, the subjects began the test. They were told that their main
objective was to remove as much soil as possible from the trench during each two-minute run. They
were also instructed that they had the secondary goals of (1) dumping each pile of soil as closely
together as possible and (2) entering the trench as cleanly as possible without clipping the sides of
the trench. Each subject was given five minutes to warm up with the first interface (the order the
subjects used each interface was reversed for each subject). Then they excavated as much soil as
possible during 5 two-minute runs. The process was then repeated using the second interface. After
they completed excavation, the subjects filled out a survey on how they perceived each controller.
The average age for subjects was 25 and ranged from 21 to 33. There were 18 males and 7 females.
One operator had 10 years of experience and the rest were novices. Twenty-four of the 25 subjects
were right-handed.
Results
There is no standard or single way to measure operator effectiveness. Several measures were taken
that have bearing on how well the operator was able to perform the assigned tasks. Depending on
the task, certain measures may be more relevant than others.
Achieving Stated Goals
Some of the more obvious ways to evaluate performance are to measure how well operators reached
their goals. The main objective was to remove as much soil as possible from the trench. All of the
subjects, including the expert operator (subject 12), were able remove more soil with the Phantom
than with the joysticks. The average for all subjects for the Phantom was 1.57 cubic yards, or about
10.2 full buckets. The average for the joysticks was 0.867 cubic yards, or about 5.62 full buckets. A
small trenching bucket was used and the simulation limited the maximum amount of soil in the
bucket to 0.154 cubic yards.
Average Amount of Soil Removed from the Trench
3
joysticks
Cubic Yards
2.5
phantom
2
1.5
1
0.5
0
1 2 3 4 5
6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
Subject Number
Fig. 4: Average amount of soil removed by each subject using the joystick and Phantom interfaces
Five minutes of practice clearly did not give operators enough time to master either input device. A
best fit line was run through each subject’s data to give a feel for how much the operators were
continuing to learn as the test progressed. For the joysticks, the average slope of the line was 0.403
cu. yds./trial, meaning that on average, the operators were removing 2.6 more full bucket loads of
soil in the fifth run than in the first. The slope for each of the 25 subjects was positive, meaning that
all of the subjects improved over time. For the Phantom, the average slope was 0.037 cu. yds/trial,
so on average, the operators were removing 1.2 more full buckets of soil in the fifth run than the
first. Also, only 15 subjects had positive slopes, meaning that 1/3 of the operators did not improve
with time. The order that the subjects used the Phantom and the joysticks was reversed for each
subject. The odd numbered subjects (those that used the Phantom first) outperformed their even
numbered counterparts by 8.0% with the joysticks and by 0.7% with the Phantom.
One of the secondary goals given to the operators was to dump all the soil removed from the trench
in the same location. Eighteen of the subjects had a lower standard deviation of pile locations with
the Phantom, including the expert operator (subject 12). Overall the average standard deviation of
the piles for the Phantom was 10.1 inches and 13.0 inches for the joysticks.
Average of Pile Standard Deviation
40
joysticks
phantom
35
Inches
30
25
20
15
10
5
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
Subject Number
Fig. 5: Average standard deviation of the spoil piles for each subject using the joystick and
Phantom interfaces
Fuel Efficiency vs. Task Efficiency
When measuring fuel efficiency of a machine, the machine is run on a standardized cycle. For
example, a car is driven through the standardized city and highway drive cycles to determine its fuel
efficiency. Automobiles’ primary task is to carry people from one point to another. To measure how
well an automobile performs this task in terms of fuel consumption, a typical human input is given
to the vehicle and the amount of fuel consumed is measured. Fuel efficiency therefore is a measure
of how much fuel a vehicle uses to complete its task, or in other words, its task efficiency. In other
contexts, the connection between fuel efficiency and task efficiency is not 1:1.
Mobile hydraulic equipment tasks vary greatly. As with automobiles, the actual efficiency is
dependent on the operator. Just like automobiles, a valid way to determine the fuel efficiency of a
piece of equipment is to give the machine a typical human input and measure how much fuel is
consumed. For mobile hydraulic machinery, fuel efficiency is often presented to the consumer in
terms of amount of fuel per time. This is valid if there is a strong correlation between time on each
machine and amount of the task completed. This is generally true, in that despite the make of the
machine, and operator completes the task in the same amount of time (i.e. it takes the same time to
complete a benching task using a twenty-ton excavator from Manufacturer A as using a twenty-ton
machine from Manufacturer B).
However, if task completion time varies widely from one machine to another, then this view of fuel
efficiency is deceptive. If the operator interface allows the operator to complete the same job in less
time but by burning more fuel per hour, then the amount of fuel per hour no longer reflects the
critical measure of amount of fuel required per task. For example, if a new interface allows the
operator to complete the same task in 25% of the time required using the standard interface, but also
burns fuel twice as fast, then task efficiency has doubled even though fuel efficiency (in terms of
fuel per time) is halved. Task efficiency determines how much fuel is consumed on the job, not fuel
efficiency.
With this view in mind, task efficiency measurements are more important than fuel efficiency
measurements. Fuel efficiency was designed as a way to measure task efficiency – if you knew how
long a task would take on the jobsite and knew the fuel efficiency of the machine being used, then
you could calculate the task efficiency in terms of fuel used to complete the task. If the time to
complete the task varied with machine, a true task efficiency calculation would need to be done.
For the experiment discussed here, the task efficiency was measured in terms of soil removed per
unit of energy. The amount of soil removed per unit fuel would be a better measure, but the
excavator simulator does not currently have a model of the engine. Instead the power output of the
pumps was recorded and used. Task efficiency information is also often given to consumers in
terms of ton of soil per liter of fuel. There does not exist a standardized cycle for excavators, and so
each manufacturer tends to give the best results of the best operators. There is no combined measure
that combines task efficiency with task time, which would be a way to measure operator
effectiveness.
Task Efficiency (Soil/Energy)
1.8
joysticks
phantom
Cubic yds/hp hr
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0
1
3
5
7
9
11
13
15
17
19
21
23
25
Subject Number
Fig. 6: Task efficiency for all 25 subjects using both the joystick and Phantom interfaces
Nineteen of the subjects, including the expert operator, were more task efficient with the Phantom.
The average standard deviation for the joysticks was 0.161 cu. yds./hp hr while the standard
deviation for the phantom was 0.135 cu. yds./hp hr, showing that operator performance was more
consistent with the Phantom.
Overall Performance
To better examine improvements in operator effectiveness, several variables were recorded. Some
of these variables are better high, some are better the lower they are, and some of them are
important only when compared with others.
The number of piles dumped (the number of times the operator scooped soil into the bucket and
then dumped it) was 69% greater with the Phantom. The load size, or the amount of soil loaded into
the bucket on each cycle was 6% greater with the Phantom. The number of excavator functions
(swing, boom, arm, bucket) being used at over 10% of the maximum, was 224% greater with the
Phantom. The standard deviation in pile location was 23% less (better) with the Phantom. As
mentioned, all the subjects removed more soil with the Phantom than with the joysticks – on
average 81% more. And the operators removed 81% more soil using 42% more energy, resulting in
an 18% increase in task efficiency. The number of piles accidentally dropped back into the trench
varied widely, and 11% less were dropped when the joysticks were used.
Normalized Measures
3
joystick
phantom
2.5
2
1.5
1
0.5
0
Soil/Energy
Soil Removed
Pile Stdev
Simultaneous
Functions
Load Size
# of Piles
Piles Dropped
Fig. 7: Measurements comparing the standard joystick interface to the Phantom. All measurements
have been normalized using the joystick performance.
Operator Survey
The survey given to the subjects afterwards asked the subjects to rate which of the controllers better
aligned with each of the following statements: easier to learn, gave more accurate bucket control,
faster to dig with, easier to use, more comfortable, and their personal preference.
Operator Survey
Number of Subjects
25
20
15
10
5
0
Easier to learn
Gave more
accurate
bucket control
Faster to dig
with
joysticks
Easier to use
More
comfortable
Personal
preference
phantom
Fig. 8: Number of subjects that selected the interface that better agreed with the statement beneath
The operators were also asked to state why they selected each interface for their choices in the
survey. They repeatedly noted that the Phantom interface was more intuitive and required less focus
or thinking to operate. They also noted that they were never confused on which way to move the
Phantom, whereas with the joysticks, many said that they often weren’t sure which joystick or
which direction on a specific joystick to move. Several noted that the Phantom was too sensitive to
small motions and felt that the joysticks gave them better control. Almost all noted that the joysticks
gave them the ability to hold the excavator arm still. Most found the joysticks more comfortable,
although some preferred that they could operate the Phantom with one hand and that it wasn’t as
stiff as the joysticks.
Conclusion
More intuitive operator interfaces can improve the fuel efficiency, or better said, task efficiency, of
mobile hydraulic equipment. In a 25-subject test using an excavator simulator, operators removed
81% more soil using 42% more energy. This results in an 18% increase in task efficiency using the
exact same machine. This far exceeds current expectations of fuel efficiency increases by
redesigning pumps, motors, or control systems to be more efficient. Future work should include
determining what the underlying laws are for the design of human-machine interfaces. Further
testing should be preformed, especially with experienced operators and on actual excavators. Before
industrial implementation, costs should be evaluated and safety measures designed and
implemented.
Acknowledgements
This work was made possible through the Center for Compact and Efficient Fluid Power (CCEFP).
There are several others that deserve our thanks and recognition: Scott Schuh and Jim Bruer at
Bobcat, Inc. for arranging the donation of a mini-excavator to our research, Jeff Herrin and Scott
Jensen at Sauer-Danfoss for sending us electronic joysticks, J.D. Huggins for heavily assisting in
the design and construction of the operator workstation, and Chris Williamson and Josh
Zimmerman for assistance in modelling the dynamics of the excavator.
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Systems, pp. 239–246
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Excavator. M.S. thesis, Georgia Institute of Technology Woodruff School of Mechanical
Engineering.
Williamson, C.; Zimmerman, J.; Ivantysynova, M. (2008). Efficiency Study of an Excavator
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